drjrm3 drjrm3 - 2 months ago 23
R Question

Vectorize min() for matrix

I'm hoping to vectorize the following loop:

for (i in 1:n) {
for (j in 1:m) {
temp_mat[i,j]=min(temp_mat[i,j],1);
}
}


I thought I could do
temp_mat=min(temp_mat,1)
, but this is not giving me the desired result. Is there a way to vectorize this loop to make it much faster?

Answer

Just use temp_mat <- pmin(temp_mat, 1). See ?pmin for more use of parallel minima.

Example:

set.seed(0); A <- matrix(sample(1:3, 25, replace = T), 5)
#> A
#     [,1] [,2] [,3] [,4] [,5]
#[1,]    3    1    1    3    3
#[2,]    1    3    1    2    3
#[3,]    2    3    1    3    1
#[4,]    2    2    3    3    2
#[5,]    3    2    2    2    1
B <- pmin(A, 2)
#> B
#     [,1] [,2] [,3] [,4] [,5]
#[1,]    2    1    1    2    2
#[2,]    1    2    1    2    2
#[3,]    2    2    1    2    1
#[4,]    2    2    2    2    2
#[5,]    2    2    2    2    1

update

Since you have background in computational science, I would like to provide more information.

pmin is fast, but is far from high performance. Its prefix "parallel" only suggests element-wise. The meaning of "vectorization" in R is not the same as "SIMD vectorization" in HPC. R is an interpreted language, so "vectorization" in R means opting for C level loop rather than R level loop. Therefore, pmin is just coded with a trivial C loop.

Real high performance computing should benefit from SIMD vectorization. I believe you know SSE/AVX intrinsics. So if you write a simple C code, using _mm_min_pd from SSE2, you will get ~2 times speedup from pmin; if you see _mm256_min_pd from AVX, you will get ~4 times speedup from pmin.

Unfortunately, R itself can not do any SIMD. I have an answer to a post at Does R leverage SIMD when doing vectorized calculations? regarding this issue. For your question, even if you link your R to a HPC BLAS, pmin will not benefit from SIMD, simply because pmin does not involve any BLAS operations. So a better bet is to write compiled code yourself.